Summary of Linear Projections Of Teacher Embeddings For Few-class Distillation, by Noel Loo et al.
Linear Projections of Teacher Embeddings for Few-Class Distillation
by Noel Loo, Fotis Iliopoulos, Wei Hu, Erik Vee
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel knowledge distillation method called Learning Embedding Linear Projections (LELP) that leverages the structure of final-layer representations in teacher models. LELP identifies informative linear subspaces in the embedding space, splits them into pseudo-subclasses, and trains student models to replicate these pseudo-classes. The proposed method is evaluated on large-scale NLP benchmarks like Amazon Reviews and Sentiment140, demonstrating its competitiveness with or superiority over existing state-of-the-art distillation algorithms for binary and few-class problems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a new way to transfer knowledge from one model to another. This is useful because sometimes we have a big model that’s good at recognizing patterns, but it’s hard to use on smaller devices. The new method works by finding important parts of the big model’s representation and breaking them down into simpler pieces. It then trains a smaller model to recognize these simplified patterns. Tests show this approach performs well for tasks like sentiment analysis. |
Keywords
» Artificial intelligence » Distillation » Embedding » Embedding space » Knowledge distillation » Nlp
